A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a
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Department of Diagnostic Imaging , The Chaim Sheba Medical Center, Ramat Gan, Israel; 2Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; 3Intuit Israel©, Hod Hasharon, Israel; 4Emergency Room, The Chaim Sheba Medical Center, Ramat Gan, Israel; 5Hospital Management, The Chaim Sheba Medical Center, Ramat Gan, Israel.
BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications. OBJECTIVE: Evaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED. DESIGN: An institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18–100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012–December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2–30 days post ED registration). A gradient boosting model was trained on data from years 2012–2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality. KEY RESULTS: Overall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11606-019-05512-7) contains supplementary material, which is available to authorized users. Received December 21, 2018 Revised July 12, 2019 Accepted October 1, 2019
CONCLUSION: The gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED. KEY WORDS: machine learning; gradient boosting; triage; emergency department; early mortality. J Gen Intern Med DOI: 10.1007/s11606-019-05512-7 © Society of General Internal Medicine 2019
INTRODUCTION
Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes associated with ED overcrowding.1–5 Triage scores6,7 aim to optimize the waiting time and prior
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